In a recent editorial launching the new journal Science Translational Medicine, Dr. Elias Zerhouni, former director of the National Institutes of Health, remarked that despite decades of advances in our understanding of human biology and the emergence of powerful new technologies, such as genomics, the transformation of scientific discoveries into effective health interventions continues to elude us (2). There is daunting complexity when applying basic discoveries and experimental approaches to treating and preventing human disease, requiring a strong translational research (TR) agenda. He stressed the need for “more and better TR, both for the sake of our patients and because much of the research funding … comes from the primary expectation of the public that such scientific investigations will reduce the burden of disease” (2, p. 1). Translation of promising scientific discoveries into day-to-day practice is slow and uncertain (3), with only a few scientists willing to venture into the translation gap, sometimes referred to as the “valley of death” (4, p. 840). Perhaps no field has generated higher expectations, deeper frustrations, and more “translation anxiety” than advances in human genomics. In 2003, Dr. Claude Lenfant, former director of the US National Heart, Lung, and Blood Institute, remarked that the gap between what we know about diseases and what we do to prevent and treat them will likely grow wider as new technologies emerge, as there is plenty of evidence that even established interventions have been “lost in translation” (5, p. 868). He asked, “Let's be realistic: If we didn't do it with aspirin, how can we expect to do it with DNA?” (5, p. 874). In public health, even with decades-long knowledge, cigarette smoking, physical inactivity, and poor diet are the main contributors to the global burden of common chronic diseases, such as diabetes and coronary heart disease. Yet, progress has been slow in translating this knowledge into a reduction of the burden of these diseases (6). This may be due to many forces other than the lack of data and scientific evidence, including political, social, legal, and economical. In this paper, we present a framework for epidemiology as a fundamental building block of the TR enterprise, using illustrations from the emerging field of human genomics. The emerging field of genomics offers a nice example, because it has the advantage of showing the potential contribution of epidemiology to all phases of TR. Moreover, it exemplifies the difficulties of translation (despite high expectations) and the relatively steep decrease in the number of studies as one moves from T1 to T4. However, the framework can be applicable to all areas of health besides genomics.

A central dilemma in medicine and public health is the translation of basic discoveries into clinical practice and population health. A familiar concept in TR refers to the translation of basic laboratory discoveries to useful clinical applications as “bench to bedside” research (1, p. 211; 11, p. 859), as the “effective translation of the new knowledge, mechanisms, and techniques generated by advances in basic science research into new approaches for prevention, diagnosis, and treatment of disease” (1, p. 211). In 2006, the National Institutes of Health launched the Clinical and Translational Science Award program as a stimulus for TR, which it defined as “the process of applying discoveries generated during research in the laboratory, and in preclinical studies, to the development of trials and studies in humans” (14). Many laboratory research scientists view TR as a linear, unidirectional process for using laboratory discoveries to develop therapeutics for study in human clinical trials; however, even this form of TR is iterative, including feedback loops from bedside to bench (15). These concepts apply to the development of both therapeutic agents and biomarkers that could be used for diagnosis and early detection (e.g., prostate-specific antigen testing for early detection of prostate cancer).

Using the proposed T1–T4 translational research framework (Table 1; Figure 1), we examine the role of epidemiology in providing data to help translate initial basic discoveries into clinical and public health applications. We use the term “translational epidemiology” (TE) to describe the application of epidemiologic methods in all phases of TR (i.e., the study of disease distribution and determinants in populations). TE overlaps with and encompasses such terms as “applied epidemiology” (20), “clinical epidemiology” (21), and “field epidemiology” (22)—all referring to post-discovery research but used in various ways, depending on the context of applications of epidemiologic research, according to the purpose of the research, the phase of its application, and the determinants and outcomes of interest. TE is at the intersection of clinical and population-based research, including observational studies and randomized controlled trials (sometimes called experimental epidemiology) (23), assessing how basic discoveries can be used to improve health.

Knowledge synthesis is a systematic approach to reviewing the evidence on what we know and what we do not know, and how we know it. We have indicated the essential role for knowledge synthesis in all phases of TR by placing it at the center of Figure 1. Knowledge synthesis methods, such as meta-analysis, are becoming standard in developing evidence-based recommendations for practice (T2 research). The Cochrane Collaboration (51), an international not-for-profit and independent organization founded in 1993, produces and disseminates systematic reviews of health-care interventions and promotes the search for evidence in the form of clinical trials and other studies of interventions. It continues to play a pivotal role in developing and promoting quantitative synthesis of evidence of what works and what does not work in health-care interventions. Increasingly, other independent groups, such as the US Preventive Services Task Force, are adopting similar methods (33, 52).

Finally, we thought it would be instructive to describe the trends and types of translational research articles published in the Journal as a snapshot of the field. Although analysis of any one journal is not necessarily generalizable to the whole field, we were curious to see how much scientific discovery and translational research are published in a premier epidemiology journal. We compared the contents of 5 issues of the American Journal of Epidemiology published in 2009 with the corresponding issues published in 1999. We read the titles and abstracts of all the articles and classified them according to our TE framework, from T0 through T4 and knowledge synthesis. We assessed all the papers in each selected issue and used a simple classification of the article by purpose of the study. We applied the definitions of T0–T4 described in Table 1. If a paper addressed multiple phases of translation, we coded the highest phase of translation. For illustration, Table 2 shows examples (60–65) of each type of paper in 1999 and in 2009. The proportion of articles that could be classified as TE increased from 16% (11/69) in 1999 to 33% (22/66) in 2009 (P = 0.02 by chi-square test; Table 2). Although this exercise is by no means an exhaustive or representative analysis of the epidemiology literature, it suggests a positive trend. We are planning additional analyses of the published literature in epidemiology, and clinical and public health journals could help to clarify the trends in translational research and the contributions of epidemiology to the field. It is possible that each journal has its own preferred niche in the T1–T4 continuum, and an evaluation of many journals could give a wider perspective about the interface of translational research and epidemiology.